The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
The strength of weak learnability
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In the citing paper
“The general principle behind boosting is that a combination of many weak learners yields a more powerful predictor than a single strong learner on its own (Freund & Schapire, 1997;Schapire, 1990).”
From Customer price sensitivities in competitive insurance markets (2022) · cites this paper as Schapire, 1990
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Boosting is originally described inSchapire (1990) andFreund (1995) for classification problems to improve the performance of a set of weak learners.”
From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as Schapire (1990)
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Citations
Cites
Probability Inequalities for sums of Bounded Random Variables
Hoeffding, W. (1994) · Springer series in statistics · doi:10.1007/978-1-4612-0865-5_26 · Cited by 6,683
Probability Inequalities for Sums of Bounded Random Variables
Hoeffding, W. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500830 · Cited by 4,869
Valiant, L. G. (1984) · ACM eBooks · doi:10.1145/3749740.3749746 · Cited by 4,211
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Angluin, D. (1988) · Machine Learning · doi:10.1023/a:1022821128753 · Cited by 1,918
Learnability and the Vapnik-Chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989) · Journal of the ACM · doi:10.1145/76359.76371 · Cited by 1,839
Probability Inequalities for Sums of Bounded Random Variables
Hoeffding, W. (1963) · Journal of the American Statistical Association · doi:10.2307/2282952 · Cited by 1,168
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987) · Information Processing Letters · doi:10.1016/0020-0190(87)90114-1 · Cited by 1,054
Rivest, R. L. (1987) · Machine Learning · doi:10.1023/a:1022607331053 · Cited by 894
Finding patterns common to a set of strings
Angluin, D. (1980) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(80)90041-0 · Cited by 609
Flagged first, then by citations
Research works printed but not linked: 1
- Haussler, D. (1988).Space efficient learning algorithms (Technical Report UCSC-CRL-88?2). Santa Cruz, CA: University of California, Baskin Center for Computer Engineering and Information Sciences.
Cited by
Sorted by citations
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Self-citationDeep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189
Self-citation
Metadata
- Authors
- Schapire, R. E.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Schapire, Robert E.
- ORCID
- Schapire, Robert E.
- Venue
- Machine Learning
- Published
- 1990-06
- Volume
- 5
- Issue
- 2
- Pages
- 197-227
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2093717447
- Cited by
- 4,600
- References
- 22
- Citations by year
- 2026: 106 · 2025: 211 · 2024: 195 · 2023: 226 · 2022: 226 · 2021: 255 · 2020: 248 · 2019: 257 · 2018: 195 · 2017: 173 · 2016: 173 · 2015: 188 · 2014: 205 · 2013: 197 · 2012: 187
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Funders
- Siemens USA
- Defense Advanced Research Projects Agency
- Army Research Office (DAAL03-86-K-0171)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- PAC learning, boosting, distribution-free learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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